E-commerce (Online Retail)

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import math

import warnings
warnings.filterwarnings("ignore")

# yahoo finance data 
import yfinance as yf
yf.pdr_override()

# input
# Online Gaming
title = "Online Shopping"
symbols = ['AMZN', 'BABA', 'JD', 'W', 'CPRT', 'VIPS', 'EXPE', 'STMP', 'CMPR', 'MMYT', 'SSTK', 'GRPN', 'LQDT', 'TOUR', 'ETSY']
start = '2018-01-01'
end = '2020-06-26'

df = pd.DataFrame()
for s in symbols:
    df[s] = yf.download(s,start,end)['Adj Close']

[*********************100%***********************]  1 of 1 completed
[*********************100%***********************]  1 of 1 completed
[*********************100%***********************]  1 of 1 completed
[*********************100%***********************]  1 of 1 completed
[*********************100%***********************]  1 of 1 completed
[*********************100%***********************]  1 of 1 completed
[*********************100%***********************]  1 of 1 completed
[*********************100%***********************]  1 of 1 completed
[*********************100%***********************]  1 of 1 completed
[*********************100%***********************]  1 of 1 completed
[*********************100%***********************]  1 of 1 completed
[*********************100%***********************]  1 of 1 completed
[*********************100%***********************]  1 of 1 completed
[*********************100%***********************]  1 of 1 completed
[*********************100%***********************]  1 of 1 completed

from datetime import datetime
from dateutil import relativedelta

d1 = datetime.strptime(start, "%Y-%m-%d")
d2 = datetime.strptime(end, "%Y-%m-%d")
delta = relativedelta.relativedelta(d2,d1)
print('How many years of investing?')
print('%s years' % delta.years)

How many years of investing?
2 years

number_of_years = delta.years

days = (df.index[-1] - df.index[0]).days
days

905

df.head()

	AMZN	BABA	JD	W	CPRT	VIPS	EXPE	STMP	CMPR	MMYT	SSTK	GRPN	LQDT	TOUR	ETSY
Date
2018-01-02	1189.010010	183.649994	43.279999	81.019997	43.599998	12.50	118.357040	187.350006	121.250000	31.150000	40.635517	104.800003	4.70	7.72	20.83
2018-01-03	1204.199951	184.000000	43.509998	78.449997	43.389999	12.20	122.012444	188.949997	123.470001	30.799999	41.485981	103.000000	4.70	7.76	20.41
2018-01-04	1209.589966	185.710007	43.669998	79.300003	43.740002	12.01	122.957962	186.800003	124.839996	29.350000	41.785049	102.400002	4.80	7.77	20.23
2018-01-05	1229.140015	190.699997	45.639999	80.750000	43.529999	12.37	123.913246	187.500000	123.620003	30.650000	41.299068	104.599998	4.85	7.79	20.25
2018-01-08	1246.869995	190.330002	46.099998	83.050003	43.549999	13.58	124.975761	190.250000	124.930000	31.549999	40.747665	104.400002	4.90	7.80	20.32

df.tail()

	AMZN	BABA	JD	W	CPRT	VIPS	EXPE	STMP	CMPR	MMYT	SSTK	GRPN	LQDT	TOUR	ETSY
Date
2020-06-19	2675.010010	220.639999	58.639999	209.990005	84.410004	21.190001	82.059998	175.580002	76.930000	17.850000	35.480000	21.590000	5.84	1.100	95.389999
2020-06-22	2713.820068	221.410004	59.090000	208.059998	84.660004	21.110001	83.120003	185.470001	78.959999	17.020000	35.880001	20.059999	6.44	1.060	96.300003
2020-06-23	2764.409912	228.750000	59.919998	207.529999	84.529999	20.650000	86.599998	180.309998	79.510002	17.420000	36.009998	21.250000	6.45	1.180	101.220001
2020-06-24	2734.399902	226.220001	59.840000	200.710007	81.309998	20.240000	81.239998	179.050003	80.589996	16.940001	35.169998	19.889999	6.33	1.295	98.169998
2020-06-25	2754.580078	222.160004	60.509998	205.119995	81.500000	19.709999	82.720001	175.000000	81.290001	17.270000	34.820000	20.219999	6.24	1.350	101.279999

plt.figure(figsize=(12,8))
plt.plot(df)
plt.title(title + ' Closing Price')
plt.legend(labels=df.columns)

<matplotlib.legend.Legend at 0x159a253c2e8>

# Normalize the data
normalize = (df - df.min())/ (df.max() - df.min())

plt.figure(figsize=(18,12))
plt.plot(normalize)
plt.title(title + ' Stocks Normalize')
plt.legend(labels=normalize.columns)

<matplotlib.legend.Legend at 0x159a27d9a20>

stock_rets = df.pct_change().dropna()

plt.figure(figsize=(12,8))
plt.plot(stock_rets)
plt.title(title + ' Stocks Returns')
plt.legend(labels=stock_rets.columns)

<matplotlib.legend.Legend at 0x159a25eda58>

plt.figure(figsize=(12,8))
plt.plot(stock_rets.cumsum())
plt.title(title + ' Stocks Returns Cumulative Sum')
plt.legend(labels=stock_rets.columns)

<matplotlib.legend.Legend at 0x159a2670c50>

sns.set(style='ticks')
ax = sns.pairplot(stock_rets, diag_kind='hist')

nplot = len(stock_rets.columns)
for i in range(nplot) :
    for j in range(nplot) :
        ax.axes[i, j].locator_params(axis='x', nbins=6, tight=True)

ax = sns.PairGrid(stock_rets)
ax.map_upper(plt.scatter, color='purple')
ax.map_lower(sns.kdeplot, color='blue')
ax.map_diag(plt.hist, bins=30)
for i in range(nplot) :
    for j in range(nplot) :
        ax.axes[i, j].locator_params(axis='x', nbins=6, tight=True)

plt.figure(figsize=(10,10))
corr = stock_rets.corr()

# plot the heatmap
sns.heatmap(corr, 
        xticklabels=corr.columns,
        yticklabels=corr.columns,
            cmap="Reds")

<matplotlib.axes._subplots.AxesSubplot at 0x159b7b3a828>

# Box plot
stock_rets.plot(kind='box',figsize=(24,8))

<matplotlib.axes._subplots.AxesSubplot at 0x159b7b7ca58>

rets = stock_rets.dropna()

plt.figure(figsize=(16,8))
plt.scatter(rets.std(), rets.mean(),alpha = 0.5)

plt.title('Stocks Risk & Returns')
plt.xlabel('Risk')
plt.ylabel('Expected Returns')
plt.grid(which='major')

for label, x, y in zip(rets.columns, rets.std(), rets.mean()):
    plt.annotate(
        label, 
        xy = (x, y), xytext = (50, 50),
        textcoords = 'offset points', ha = 'right', va = 'bottom',
        arrowprops = dict(arrowstyle = '-', connectionstyle = 'arc3,rad=-0.3'))

rets = stock_rets.dropna()
area = np.pi*20.0

sns.set(style='darkgrid')
plt.figure(figsize=(16,8))
plt.scatter(rets.std(), rets.mean(), s=area)
plt.xlabel("Risk", fontsize=15)
plt.ylabel("Expected Return", fontsize=15)
plt.title("Return vs. Risk for Stocks", fontsize=20)

for label, x, y in zip(rets.columns, rets.std(), rets.mean()) : 
    plt.annotate(label, xy=(x,y), xytext=(50, 0), textcoords='offset points',
                arrowprops=dict(arrowstyle='-', connectionstyle='bar,angle=180,fraction=-0.2'),
                bbox=dict(boxstyle="round", fc="w"))

rest_rets = rets.corr()
pair_value = rest_rets.abs().unstack()
pair_value.sort_values(ascending = False)

ETSY  ETSY    1.000000
TOUR  TOUR    1.000000
BABA  BABA    1.000000
JD    JD      1.000000
W     W       1.000000
CPRT  CPRT    1.000000
VIPS  VIPS    1.000000
EXPE  EXPE    1.000000
CMPR  CMPR    1.000000
MMYT  MMYT    1.000000
SSTK  SSTK    1.000000
GRPN  GRPN    1.000000
LQDT  LQDT    1.000000
STMP  STMP    1.000000
AMZN  AMZN    1.000000
BABA  JD      0.728167
JD    BABA    0.728167
AMZN  BABA    0.623540
BABA  AMZN    0.623540
ETSY  W       0.584408
W     ETSY    0.584408
AMZN  JD      0.529407
JD    AMZN    0.529407
VIPS  BABA    0.521914
BABA  VIPS    0.521914
VIPS  JD      0.511976
JD    VIPS    0.511976
CPRT  EXPE    0.493708
EXPE  CPRT    0.493708
CPRT  CMPR    0.491147
                ...   
LQDT  STMP    0.138022
STMP  LQDT    0.138022
MMYT  STMP    0.132666
STMP  MMYT    0.132666
VIPS  LQDT    0.132209
LQDT  VIPS    0.132209
STMP  W       0.130498
W     STMP    0.130498
STMP  GRPN    0.122858
GRPN  STMP    0.122858
STMP  CPRT    0.118703
CPRT  STMP    0.118703
SSTK  TOUR    0.117426
TOUR  SSTK    0.117426
STMP  TOUR    0.112904
TOUR  STMP    0.112904
STMP  JD      0.110647
JD    STMP    0.110647
MMYT  LQDT    0.110364
LQDT  MMYT    0.110364
TOUR  VIPS    0.095004
VIPS  TOUR    0.095004
CMPR  VIPS    0.086648
VIPS  CMPR    0.086648
LQDT  GRPN    0.077633
GRPN  LQDT    0.077633
STMP  VIPS    0.076341
VIPS  STMP    0.076341
LQDT  TOUR    0.070838
TOUR  LQDT    0.070838
Length: 225, dtype: float64

# Normalized Returns Data
Normalized_Value = ((rets[:] - rets[:].min()) /(rets[:].max() - rets[:].min()))
Normalized_Value.head()

	AMZN	BABA	JD	W	CPRT	VIPS	EXPE	STMP	CMPR	MMYT	SSTK	GRPN	LQDT	TOUR	ETSY
Date
2018-01-03	0.529708	0.539969	0.488254	0.338183	0.432239	0.373907	0.662419	0.475557	0.392456	0.478691	0.580312	0.593523	0.344545	0.359829	0.326807
2018-01-04	0.481922	0.589834	0.481474	0.399395	0.473252	0.391863	0.612131	0.459399	0.383051	0.394830	0.526239	0.609358	0.383921	0.350828	0.353824
2018-01-05	0.549212	0.708469	0.653091	0.410111	0.432288	0.488922	0.612174	0.471669	0.355844	0.608614	0.451991	0.647456	0.363823	0.353800	0.377182
2018-01-08	0.539206	0.514010	0.507989	0.424780	0.449035	0.633487	0.613925	0.480527	0.382401	0.573684	0.445208	0.614817	0.363624	0.350817	0.383060
2018-01-09	0.483071	0.543773	0.468939	0.352634	0.442456	0.370129	0.579519	0.509989	0.369211	0.553184	0.497828	0.580069	0.382314	0.315241	0.442805

Normalized_Value.corr()

	AMZN	BABA	JD	W	CPRT	VIPS	EXPE	STMP	CMPR	MMYT	SSTK	GRPN	LQDT	TOUR	ETSY
AMZN	1.000000	0.623540	0.529407	0.395002	0.443066	0.341891	0.308963	0.211119	0.225842	0.304840	0.403535	0.214087	0.194879	0.188695	0.449388
BABA	0.623540	1.000000	0.728167	0.382806	0.429878	0.521914	0.362231	0.175112	0.248276	0.388620	0.358317	0.269944	0.145286	0.271992	0.398056
JD	0.529407	0.728167	1.000000	0.329885	0.401921	0.511976	0.334538	0.110647	0.252861	0.356663	0.334997	0.237358	0.146901	0.211198	0.336071
W	0.395002	0.382806	0.329885	1.000000	0.418870	0.186704	0.365491	0.130498	0.384676	0.367650	0.242927	0.302610	0.183630	0.219105	0.584408
CPRT	0.443066	0.429878	0.401921	0.418870	1.000000	0.198783	0.493708	0.118703	0.491147	0.308657	0.246260	0.367466	0.171930	0.212513	0.376194
VIPS	0.341891	0.521914	0.511976	0.186704	0.198783	1.000000	0.160568	0.076341	0.086648	0.282590	0.209796	0.150447	0.132209	0.095004	0.226079
EXPE	0.308963	0.362231	0.334538	0.365491	0.493708	0.160568	1.000000	0.191786	0.419566	0.469207	0.223167	0.443468	0.156999	0.245878	0.321978
STMP	0.211119	0.175112	0.110647	0.130498	0.118703	0.076341	0.191786	1.000000	0.175789	0.132666	0.176137	0.122858	0.138022	0.112904	0.217647
CMPR	0.225842	0.248276	0.252861	0.384676	0.491147	0.086648	0.419566	0.175789	1.000000	0.303116	0.150943	0.365095	0.286725	0.254538	0.308569
MMYT	0.304840	0.388620	0.356663	0.367650	0.308657	0.282590	0.469207	0.132666	0.303116	1.000000	0.161137	0.351165	0.110364	0.239634	0.289695
SSTK	0.403535	0.358317	0.334997	0.242927	0.246260	0.209796	0.223167	0.176137	0.150943	0.161137	1.000000	0.154366	0.182702	0.117426	0.247327
GRPN	0.214087	0.269944	0.237358	0.302610	0.367466	0.150447	0.443468	0.122858	0.365095	0.351165	0.154366	1.000000	0.077633	0.204033	0.223572
LQDT	0.194879	0.145286	0.146901	0.183630	0.171930	0.132209	0.156999	0.138022	0.286725	0.110364	0.182702	0.077633	1.000000	0.070838	0.162882
TOUR	0.188695	0.271992	0.211198	0.219105	0.212513	0.095004	0.245878	0.112904	0.254538	0.239634	0.117426	0.204033	0.070838	1.000000	0.171830
ETSY	0.449388	0.398056	0.336071	0.584408	0.376194	0.226079	0.321978	0.217647	0.308569	0.289695	0.247327	0.223572	0.162882	0.171830	1.000000

normalized_rets = Normalized_Value.corr()
normalized_pair_value = normalized_rets.abs().unstack()
normalized_pair_value.sort_values(ascending = False)

ETSY  ETSY    1.000000
TOUR  TOUR    1.000000
BABA  BABA    1.000000
JD    JD      1.000000
W     W       1.000000
CPRT  CPRT    1.000000
VIPS  VIPS    1.000000
EXPE  EXPE    1.000000
CMPR  CMPR    1.000000
MMYT  MMYT    1.000000
SSTK  SSTK    1.000000
GRPN  GRPN    1.000000
LQDT  LQDT    1.000000
STMP  STMP    1.000000
AMZN  AMZN    1.000000
BABA  JD      0.728167
JD    BABA    0.728167
AMZN  BABA    0.623540
BABA  AMZN    0.623540
ETSY  W       0.584408
W     ETSY    0.584408
AMZN  JD      0.529407
JD    AMZN    0.529407
VIPS  BABA    0.521914
BABA  VIPS    0.521914
VIPS  JD      0.511976
JD    VIPS    0.511976
CPRT  EXPE    0.493708
EXPE  CPRT    0.493708
CPRT  CMPR    0.491147
                ...   
LQDT  STMP    0.138022
STMP  LQDT    0.138022
MMYT  STMP    0.132666
STMP  MMYT    0.132666
VIPS  LQDT    0.132209
LQDT  VIPS    0.132209
STMP  W       0.130498
W     STMP    0.130498
STMP  GRPN    0.122858
GRPN  STMP    0.122858
STMP  CPRT    0.118703
CPRT  STMP    0.118703
SSTK  TOUR    0.117426
TOUR  SSTK    0.117426
STMP  TOUR    0.112904
TOUR  STMP    0.112904
STMP  JD      0.110647
JD    STMP    0.110647
MMYT  LQDT    0.110364
LQDT  MMYT    0.110364
TOUR  VIPS    0.095004
VIPS  TOUR    0.095004
CMPR  VIPS    0.086648
VIPS  CMPR    0.086648
LQDT  GRPN    0.077633
GRPN  LQDT    0.077633
STMP  VIPS    0.076341
VIPS  STMP    0.076341
LQDT  TOUR    0.070838
TOUR  LQDT    0.070838
Length: 225, dtype: float64

print("Stock returns: ")
print(rets.mean())
print('-' * 50)
print("Stock risks:")
print(rets.std())

Stock returns: 
AMZN    0.001556
BABA    0.000537
JD      0.000933
W       0.002751
CPRT    0.001241
VIPS    0.001450
EXPE   -0.000094
STMP    0.001560
CMPR    0.000208
MMYT   -0.000296
SSTK    0.000071
GRPN   -0.001366
LQDT    0.001064
TOUR   -0.002046
ETSY    0.003266
dtype: float64
--------------------------------------------------
Stock risks:
AMZN    0.020436
BABA    0.021545
JD      0.028204
W       0.051164
CPRT    0.021769
VIPS    0.038174
EXPE    0.030316
STMP    0.053629
CMPR    0.041852
MMYT    0.036117
SSTK    0.025170
GRPN    0.048681
LQDT    0.035609
TOUR    0.038925
ETSY    0.038672
dtype: float64

table = pd.DataFrame()
table['Returns'] = rets.mean()
table['Risk'] = rets.std()
table.sort_values(by='Returns')

	Returns	Risk
TOUR	-0.002046	0.038925
GRPN	-0.001366	0.048681
MMYT	-0.000296	0.036117
EXPE	-0.000094	0.030316
SSTK	0.000071	0.025170
CMPR	0.000208	0.041852
BABA	0.000537	0.021545
JD	0.000933	0.028204
LQDT	0.001064	0.035609
CPRT	0.001241	0.021769
VIPS	0.001450	0.038174
AMZN	0.001556	0.020436
STMP	0.001560	0.053629
W	0.002751	0.051164
ETSY	0.003266	0.038672

table.sort_values(by='Risk')

	Returns	Risk
AMZN	0.001556	0.020436
BABA	0.000537	0.021545
CPRT	0.001241	0.021769
SSTK	0.000071	0.025170
JD	0.000933	0.028204
EXPE	-0.000094	0.030316
LQDT	0.001064	0.035609
MMYT	-0.000296	0.036117
VIPS	0.001450	0.038174
ETSY	0.003266	0.038672
TOUR	-0.002046	0.038925
CMPR	0.000208	0.041852
GRPN	-0.001366	0.048681
W	0.002751	0.051164
STMP	0.001560	0.053629

rf = 0.01
table['Sharpe Ratio'] = (table['Returns'] - rf) / table['Risk']
table

	Returns	Risk	Sharpe Ratio
AMZN	0.001556	0.020436	-0.413201
BABA	0.000537	0.021545	-0.439209
JD	0.000933	0.028204	-0.321467
W	0.002751	0.051164	-0.141674
CPRT	0.001241	0.021769	-0.402359
VIPS	0.001450	0.038174	-0.223972
EXPE	-0.000094	0.030316	-0.332977
STMP	0.001560	0.053629	-0.157367
CMPR	0.000208	0.041852	-0.233958
MMYT	-0.000296	0.036117	-0.285071
SSTK	0.000071	0.025170	-0.394455
GRPN	-0.001366	0.048681	-0.233483
LQDT	0.001064	0.035609	-0.250961
TOUR	-0.002046	0.038925	-0.309459
ETSY	0.003266	0.038672	-0.174131

table['Max Returns'] = rets.max()

table['Min Returns'] = rets.min()

table['Median Returns'] = rets.median()

total_return = stock_rets[-1:].transpose()
table['Total Return'] = 100 * total_return
table

	Returns	Risk	Sharpe Ratio	Max Returns	Min Returns	Median Returns	Total Return
AMZN	0.001556	0.020436	-0.413201	0.094452	-0.079221	0.002077	0.738011
BABA	0.000537	0.021545	-0.439209	0.070061	-0.078093	0.001488	-1.794712
JD	0.000933	0.028204	-0.321467	0.128866	-0.112566	0.000832	1.119649
W	0.002751	0.051164	-0.141674	0.428384	-0.266830	0.002603	2.197194
CPRT	0.001241	0.021769	-0.402359	0.173528	-0.140591	0.001972	0.233677
VIPS	0.001450	0.038174	-0.223972	0.269817	-0.199470	0.000000	-2.618580
EXPE	-0.000094	0.030316	-0.332977	0.186189	-0.273862	0.000606	1.821767
STMP	0.001560	0.053629	-0.157367	0.655039	-0.577696	0.001507	-2.261940
CMPR	0.000208	0.041852	-0.233958	0.484301	-0.282708	0.001562	0.868600
MMYT	-0.000296	0.036117	-0.285071	0.211570	-0.215827	-0.000984	1.948051
SSTK	0.000071	0.025170	-0.394455	0.127418	-0.126316	0.000874	-0.995162
GRPN	-0.001366	0.048681	-0.233483	0.274194	-0.442623	-0.002805	1.659125
LQDT	0.001064	0.035609	-0.250961	0.354167	-0.186170	0.000000	-1.421803
TOUR	-0.002046	0.038925	-0.309459	0.282051	-0.150442	0.000000	4.247110
ETSY	0.003266	0.038672	-0.174131	0.262504	-0.157386	0.001878	3.167975

table['Average Return Days'] = (1 + total_return)**(1 / days) - 1
table

	Returns	Risk	Sharpe Ratio	Max Returns	Min Returns	Median Returns	Total Return	Average Return Days
AMZN	0.001556	0.020436	-0.413201	0.094452	-0.079221	0.002077	0.738011	0.000008
BABA	0.000537	0.021545	-0.439209	0.070061	-0.078093	0.001488	-1.794712	-0.000020
JD	0.000933	0.028204	-0.321467	0.128866	-0.112566	0.000832	1.119649	0.000012
W	0.002751	0.051164	-0.141674	0.428384	-0.266830	0.002603	2.197194	0.000024
CPRT	0.001241	0.021769	-0.402359	0.173528	-0.140591	0.001972	0.233677	0.000003
VIPS	0.001450	0.038174	-0.223972	0.269817	-0.199470	0.000000	-2.618580	-0.000029
EXPE	-0.000094	0.030316	-0.332977	0.186189	-0.273862	0.000606	1.821767	0.000020
STMP	0.001560	0.053629	-0.157367	0.655039	-0.577696	0.001507	-2.261940	-0.000025
CMPR	0.000208	0.041852	-0.233958	0.484301	-0.282708	0.001562	0.868600	0.000010
MMYT	-0.000296	0.036117	-0.285071	0.211570	-0.215827	-0.000984	1.948051	0.000021
SSTK	0.000071	0.025170	-0.394455	0.127418	-0.126316	0.000874	-0.995162	-0.000011
GRPN	-0.001366	0.048681	-0.233483	0.274194	-0.442623	-0.002805	1.659125	0.000018
LQDT	0.001064	0.035609	-0.250961	0.354167	-0.186170	0.000000	-1.421803	-0.000016
TOUR	-0.002046	0.038925	-0.309459	0.282051	-0.150442	0.000000	4.247110	0.000046
ETSY	0.003266	0.038672	-0.174131	0.262504	-0.157386	0.001878	3.167975	0.000034

initial_value = df.iloc[0]
ending_value = df.iloc[-1]
table['CAGR'] = ((ending_value / initial_value) ** (252.0 / days)) -1
table

	Returns	Risk	Sharpe Ratio	Max Returns	Min Returns	Median Returns	Total Return	Average Return Days	CAGR
AMZN	0.001556	0.020436	-0.413201	0.094452	-0.079221	0.002077	0.738011	0.000008	0.263569
BABA	0.000537	0.021545	-0.439209	0.070061	-0.078093	0.001488	-1.794712	-0.000020	0.054438
JD	0.000933	0.028204	-0.321467	0.128866	-0.112566	0.000832	1.119649	0.000012	0.097807
W	0.002751	0.051164	-0.141674	0.428384	-0.266830	0.002603	2.197194	0.000024	0.295187
CPRT	0.001241	0.021769	-0.402359	0.173528	-0.140591	0.001972	0.233677	0.000003	0.190276
VIPS	0.001450	0.038174	-0.223972	0.269817	-0.199470	0.000000	-2.618580	-0.000029	0.135198
EXPE	-0.000094	0.030316	-0.332977	0.186189	-0.273862	0.000606	1.821767	0.000020	-0.094940
STMP	0.001560	0.053629	-0.157367	0.655039	-0.577696	0.001507	-2.261940	-0.000025	-0.018809
CMPR	0.000208	0.041852	-0.233958	0.484301	-0.282708	0.001562	0.868600	0.000010	-0.105360
MMYT	-0.000296	0.036117	-0.285071	0.211570	-0.215827	-0.000984	1.948051	0.000021	-0.151465
SSTK	0.000071	0.025170	-0.394455	0.127418	-0.126316	0.000874	-0.995162	-0.000011	-0.042096
GRPN	-0.001366	0.048681	-0.233483	0.274194	-0.442623	-0.002805	1.659125	0.000018	-0.367555
LQDT	0.001064	0.035609	-0.250961	0.354167	-0.186170	0.000000	-1.421803	-0.000016	0.082116
TOUR	-0.002046	0.038925	-0.309459	0.282051	-0.150442	0.000000	4.247110	0.000046	-0.384636
ETSY	0.003266	0.038672	-0.174131	0.262504	-0.157386	0.001878	3.167975	0.000034	0.553285

table.sort_values(by='Average Return Days')

	Returns	Risk	Sharpe Ratio	Max Returns	Min Returns	Median Returns	Total Return	Average Return Days	CAGR
VIPS	0.001450	0.038174	-0.223972	0.269817	-0.199470	0.000000	-2.618580	-0.000029	0.135198
STMP	0.001560	0.053629	-0.157367	0.655039	-0.577696	0.001507	-2.261940	-0.000025	-0.018809
BABA	0.000537	0.021545	-0.439209	0.070061	-0.078093	0.001488	-1.794712	-0.000020	0.054438
LQDT	0.001064	0.035609	-0.250961	0.354167	-0.186170	0.000000	-1.421803	-0.000016	0.082116
SSTK	0.000071	0.025170	-0.394455	0.127418	-0.126316	0.000874	-0.995162	-0.000011	-0.042096
CPRT	0.001241	0.021769	-0.402359	0.173528	-0.140591	0.001972	0.233677	0.000003	0.190276
AMZN	0.001556	0.020436	-0.413201	0.094452	-0.079221	0.002077	0.738011	0.000008	0.263569
CMPR	0.000208	0.041852	-0.233958	0.484301	-0.282708	0.001562	0.868600	0.000010	-0.105360
JD	0.000933	0.028204	-0.321467	0.128866	-0.112566	0.000832	1.119649	0.000012	0.097807
GRPN	-0.001366	0.048681	-0.233483	0.274194	-0.442623	-0.002805	1.659125	0.000018	-0.367555
EXPE	-0.000094	0.030316	-0.332977	0.186189	-0.273862	0.000606	1.821767	0.000020	-0.094940
MMYT	-0.000296	0.036117	-0.285071	0.211570	-0.215827	-0.000984	1.948051	0.000021	-0.151465
W	0.002751	0.051164	-0.141674	0.428384	-0.266830	0.002603	2.197194	0.000024	0.295187
ETSY	0.003266	0.038672	-0.174131	0.262504	-0.157386	0.001878	3.167975	0.000034	0.553285
TOUR	-0.002046	0.038925	-0.309459	0.282051	-0.150442	0.000000	4.247110	0.000046	-0.384636